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Artificial Intelligence (AI) software systems, such as Sentiment Analysis(SA) systems, typically learn from large amounts of data that may reflect humanbiases. Consequently, the machine learning model in such software systems mayexhibit unintended demographic bias based on specific characteristics (e.g.,gender, occupation, country-of-origin, etc.). Such biases manifest in an SAsystem when it predicts a different sentiment for similar texts that differonly in the characteristic of individuals described. Existing studies onrevealing bias in SA systems rely on the production of sentences from a smallset of short, predefined templates.  To address this limitation, we present BisaFinder, an approach to discoverbiased predictions in SA systems via metamorphic testing. A key feature ofBisaFinder is the automatic curation of suitable templates based on the piecesof text from a large corpus, using various Natural Language Processing (NLP)techniques to identify words that describe demographic characteristics. Next,BisaFinder instantiates new text from these templates by filling inplaceholders with words associated with a class of a characteristic (e.g.,gender-specific words such as female names, "she", "her"). These texts are usedto tease out bias in an SA system. BisaFinder identifies a bias-uncovering testcase when it detects that the SA system exhibits demographic bias for a pair oftexts, i.e., it predicts a different sentiment for texts that differ only inwords associated with a different class (e.g., male vs. female) of a targetcharacteristic (e.g., gender). Our empirical evaluation showed that BiasFindercan effectively create a larger number of fluent and diverse test cases thatuncover various biases in an SA system.

BiasFinder: Metamorphic Test Generation to Uncover Bias for Sentiment Analysis Systems